<html>
 <head>
  <meta charset="utf-8"/>
  <meta content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no" name="viewport"/>
  <title>
   主题：【书籍介绍】应用线性统计模型 上册 （英文影印版·原书第5版）  | 数螺 | NAUT IDEA
  </title>
  <link href="http://cdn.bootcss.com/bootstrap/3.3.6/css/bootstrap-theme.min.css" rel="stylesheet"/>
  <link href="http://cdn.bootcss.com/bootstrap/3.3.6/css/bootstrap.min.css" rel="stylesheet"/>
  <style type="text/css">
   #xmain img {
                  max-width: 100%;
                  display: block;
                  margin-top: 10px;
                  margin-bottom: 10px;
                }

                #xmain p {
                    line-height:150%;
                    font-size: 16px;
                    margin-top: 20px;
                }

                #xmain h2 {
                    font-size: 24px;
                }

                #xmain h3 {
                    font-size: 20px;
                }

                #xmain h4 {
                    font-size: 18px;
                }


                .header {
	           background-color: #0099ff;
	           color: #ffffff;
	           margin-bottom: 20px;
	        }

	        .header p {
                  margin: 0px;
                  padding: 10px 0;
                  display: inline-block;  
                  vertical-align: middle;
                  font-size: 16px;
               }

               .header a {
                 color: white;
               }

              .header img {
                 height: 25px;
              }
  </style>
  <script src="http://cdn.bootcss.com/jquery/3.0.0/jquery.min.js">
  </script>
  <script src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript">
   MathJax.Hub.Config({elements: ["bbpress-forums"]});
  </script>
  <script src="http://nautstatic-10007657.file.myqcloud.com/static/css/readability.min.js" type="text/javascript">
  </script>
  <script type="text/javascript">
   $(document).ready(function() {
                 var loc = document.location;
                 var uri = {
                  spec: "http://cos.name/cn/topic/417233/",
                  host: "http://cos.name",
                  prePath: "http://cos.name",
                  scheme: "http",
                  pathBase: "http://cos.name/"
                 };
    
                 var documentClone = document.cloneNode(true);
                 var article = new Readability(uri, documentClone).parse();
     
                 document.getElementById("xmain").innerHTML = article.content;
                });
  </script>
  <!-- 1466443750: Accept with keywords: (title(0.222222222222):影印版,版,模型,主题,英文,论坛,线性,原书,书籍, topn(0.2):知识点,主站,帖子,统计学,英文,教材,实际,影印版,用户名,模型,学生,例子,习题,部分,变量,回归,密码,篇幅,线性,专业,案例,数据,主题,总计,内容,论坛,原书,例题,书籍,讨论区).-->
 </head>
 <body class="topic bbpress single single-topic postid-417233 single-author sidebar" onload="">
  <div class="header">
   <div class="container">
    <div class="row">
     <div class="col-xs-6 col-sm-6 text-left">
      <a href="/databee">
       <img src="http://nautidea-10007657.cos.myqcloud.com/logo_white.png"/>
      </a>
      <a href="/databee">
       <p>
        数螺
       </p>
      </a>
     </div>
     <div class="hidden-xs col-sm-6 text-right">
      <p>
       致力于数据科学的推广和知识传播
      </p>
     </div>
    </div>
   </div>
  </div>
  <div class="container text-center">
   <h1>
    主题：【书籍介绍】应用线性统计模型 上册 （英文影印版·原书第5版）
   </h1>
  </div>
  <div class="container" id="xmain">
   <div class="hfeed site" id="page">
    <header class="site-header" id="masthead" role="banner">
     <div id="cos-logo">
      <a href="http://cos.name/cn">
       <img src="http://cos.name/cn/wp-content/themes/COS-forest/images/headers/cos-logo.png"/>
      </a>
     </div>
     <div class="navbar" id="navbar">
      <nav class="navigation main-navigation" id="site-navigation" role="navigation">
       <h3 class="menu-toggle">
        菜单
       </h3>
       <div class="menu-%e8%8f%9c%e5%8d%951-container">
        <ul class="nav-menu" id="menu-%e8%8f%9c%e5%8d%951">
         <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-home menu-item-407772" id="menu-item-407772">
          <a href="http://cos.name/cn/">
           论坛首页
          </a>
         </li>
         <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-407773" id="menu-item-407773">
          <a href="http://cos.name/cn/forums/">
           讨论区
          </a>
         </li>
         <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-407774" id="menu-item-407774">
          <a href="http://cos.name/cn/wp-login.php?action=register">
           注册
          </a>
         </li>
         <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-407819" id="menu-item-407819">
          <a href="http://cos.name/">
           主站
          </a>
         </li>
        </ul>
       </div>
      </nav>
      <!-- #site-navigation -->
     </div>
     <!-- #navbar -->
    </header>
    <!-- #masthead -->
    <div class="site-main" id="main">
     <div class="content-area" id="primary">
      <div class="site-content" id="content" role="main">
       <article class="post-417233 topic type-topic status-publish hentry topic-tag-979" id="post-417233">
        <header class="entry-header">
         <h1 class="entry-title">
          【书籍介绍】应用线性统计模型 上册 （英文影印版·原书第5版）
         </h1>
        </header>
        <!-- .entry-header -->
        <div class="entry-content">
         <div id="bbpress-forums">
          <div class="bbp-breadcrumb">
           <p>
            <a class="bbp-breadcrumb-home" href="http://cos.name/cn/">
             COS论坛 | 统计之都
            </a>
            <span class="bbp-breadcrumb-sep">
             ›
            </span>
            <a class="bbp-breadcrumb-root" href="http://cos.name/cn/forums/">
             讨论区
            </a>
            <span class="bbp-breadcrumb-sep">
             ›
            </span>
            <a class="bbp-breadcrumb-forum" href="http://cos.name/cn/forum/stat-world/">
             统计学世界
            </a>
            <span class="bbp-breadcrumb-sep">
             ›
            </span>
            <a class="bbp-breadcrumb-forum" href="http://cos.name/cn/forum/stat-world/math-stat/">
             数理统计
            </a>
            <span class="bbp-breadcrumb-sep">
             ›
            </span>
            <span class="bbp-breadcrumb-current">
             【书籍介绍】应用线性统计模型 上册 （英文影印版·原书第5版）
            </span>
           </p>
          </div>
          <div class="bbp-topic-tags">
           <p>
            Tagged:
            <a href="http://cos.name/cn/topic-tag/%e5%ba%94%e7%94%a8%e7%ba%bf%e6%80%a7%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b-%e6%9c%ba%e6%a2%b0%e5%b7%a5%e4%b8%9a%e5%87%ba%e7%89%88%e7%a4%be/" rel="tag">
             应用线性统计模型 机械工业出版社
            </a>
           </p>
          </div>
          <div class="bbp-template-notice info">
           <p class="bbp-topic-description">
            该主题包含 0 条回复，1个帖子，最后由
            <a class="bbp-author-avatar" href="http://cos.name/cn/profile/lixingpai/" rel="nofollow" title="查看lixingpai的档案">
             <img src="http://sdn.geekzu.org/avatar/d3478c26dfb25845020585a7732a577f?s=14&amp;d=monsterid&amp;r=g"/>
            </a>
            <a class="bbp-author-name" href="http://cos.name/cn/profile/lixingpai/" rel="nofollow" title="查看lixingpai的档案">
             lixingpai
            </a>
            在
            <a href="http://cos.name/cn/topic/417233/" title="【书籍介绍】应用线性统计模型 上册 （英文影印版·原书第5版）">
             1 月 之前
            </a>
            更新。
           </p>
          </div>
          <div class="bbp-pagination">
           <div class="bbp-pagination-count">
            查看 1 个帖子（总计 1 个）
           </div>
           <div class="bbp-pagination-links">
           </div>
          </div>
          <ul class="forums bbp-replies" id="topic-417233-replies">
           <li class="bbp-header">
            <div class="bbp-reply-author">
             作者
            </div>
            <!-- .bbp-reply-author -->
            <div class="bbp-reply-content">
             帖子
            </div>
            <!-- .bbp-reply-content -->
           </li>
           <!-- .bbp-header -->
           <li class="bbp-body">
            <div class="bbp-reply-header" id="post-417233">
             <div class="bbp-meta">
              <span class="bbp-reply-post-date">
               2016年5月19日 上午11:19
              </span>
              <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/417233/#post-417233">
               1 楼
              </a>
              <span class="bbp-admin-links">
              </span>
             </div>
             <!-- .bbp-meta -->
            </div>
            <!-- #post-417233 -->
            <div class="odd bbp-parent-forum-991 bbp-parent-topic-417233 bbp-reply-position-1 user-id-431708 topic-author post-417233 topic type-topic status-publish hentry topic-tag-979">
             <div class="bbp-reply-author">
              <a class="bbp-author-avatar" href="http://cos.name/cn/profile/lixingpai/" rel="nofollow" title="查看lixingpai的档案">
               <img src="http://sdn.geekzu.org/avatar/d3478c26dfb25845020585a7732a577f?s=80&amp;d=monsterid&amp;r=g"/>
              </a>
              <br/>
              <a class="bbp-author-name" href="http://cos.name/cn/profile/lixingpai/" rel="nofollow" title="查看lixingpai的档案">
               lixingpai
              </a>
              <br/>
              <div class="bbp-author-role">
               普通会员
              </div>
             </div>
             <!-- .bbp-reply-author -->
             <div class="bbp-reply-content">
              <p>
               【编辑推荐】
               <br/>
               本书是在美国大学中广泛使用的教材，已经再版至第5版，不仅深受广大师生的欢迎，而且有很大的影响，已逐步成为经典。
               <br/>
               由于篇幅较大，股起英文影印版分为上、下两册。本书深入地介绍了“应用线性统计模型”这门课程中几乎所有的关键知识，但是读起来并不艰深晦涩。书中用深入浅出的方式来讲解相关概念，同时配有大量的例题、习题以及实际案例帮助学生理解知识点。同时在帮助学生独立地解决实际问题方面，本书给人留下很深刻的印象。
               <br/>
               本书图文并茂，许多例子和习题都是经过精心挑选的，来源于生活和工程实践，丰富的数据也都取材于实际案例。因此，本书不仅适用于统计专业，也可作为商业、计量经济学等专业的参考书。
               <br/>
               本书叙述比较详尽，内容比国内教材丰富，篇幅较大，因此作为教材时刻适当选取主要内容讲授，其余可作为学生自学使用。
               <br/>
               【内容提要】
               <br/>
               本书分为三部分：第1部分简单线性回归，内容涉及单个预测变量的线性回归、利用回归和相关分析做推断、诊断和修正测度、回归分析的联合推断和其他论题以及简单线性回归分析的矩阵法等内容；第2部分多重线性回归、内容涉及多重回归Ⅰ，多重回归Ⅱ，定量和定性预测变量的回归模型、构建回归模型Ⅰ、构建回归模型Ⅱ、构建回归模型Ⅲ、时序数据中的自相关等内容；第3部分非线性回归，内容涉及非线性回归的引入和神经网络、Logistic回归、泊松回归和广义线性模型等内容。本书篇幅适中，例子涉及各个应用领域，在介绍统计思想方面比较突出，数据丰富。
               <br/>
               本书适用于高等院校统计学专业和理工科各专业本科生和研究生作为教材使用。
               <br/>
               【目录】
               <br/>
               Contents
               <br/>
               preface
               <br/>
               PART ONE
               <br/>
               SIMPLE LINEAR REGRESSION 1
               <br/>
               Chapter 1
               <br/>
               Linear Regression with One Predictor
               <br/>
               Variable 2
               <br/>
               1.1 Relations between Variables 2
               <br/>
               Functional Relation between Two
               <br/>
               Variables 2
               <br/>
               Statistical Relation between Two Variables 3
               <br/>
               1.2 Regression Models and Their Uses 5
               <br/>
               Historical Origins 5
               <br/>
               Basic Concepts 5
               <br/>
               Construction of Regression Models 7
               <br/>
               Uses of Regression Analysis 8
               <br/>
               Regression and Causality 8
               <br/>
               Use of Computers 9
               <br/>
               1.3 Simple Linear Regression Model
               <br/>
               with Distribution of Error Terms
               <br/>
               Unspecified 9
               <br/>
               Formal Statement of Model 9
               <br/>
               Important Features of Model 9
               <br/>
               Meaning of Regression Parameters 11
               <br/>
               Alternative Versions of Regression Model 12
               <br/>
               1.4 Data for Regression Analysis 12
               <br/>
               Observational Data 12
               <br/>
               Experimental Data 13
               <br/>
               Completely Randomized Design 13
               <br/>
               1.5 Overview of Steps in Regression
               <br/>
               Analysis 13
               <br/>
               1.6 Estimation of Regression Function 15
               <br/>
               Method of Least Squares 15
               <br/>
               Point Estimation of Mean Response 21
               <br/>
               Residuals 22
               <br/>
               Properties of Fitted Regression Line 23
               <br/>
               1.7 Estimation of Error Terms Variance •2 24
               <br/>
               Point Estimator of •2 24
               <br/>
               1.8 Normal Error Regression Model 26
               <br/>
               Model 26
               <br/>
               Estimation of Parameters by Method
               <br/>
               of Maximum Likelihood 27
               <br/>
               Cited References 33
               <br/>
               Problems 33
               <br/>
               Exercises 37
               <br/>
               Projects 38
               <br/>
               Chapter 2
               <br/>
               Inferences in Regression and Correlation
               <br/>
               Analysis 40
               <br/>
               2.1 Inferences Concerning •1 40
               <br/>
               Sampling Distribution of b1 41
               <br/>
               Sampling Distribution of (b1 -•1)/s{b1} 44
               <br/>
               Confidence Interval for •1 45
               <br/>
               Tests Concerning •1 47
               <br/>
               2.2 Inferences Concerning •0 48
               <br/>
               Sampling Distribution of b0 48
               <br/>
               Sampling Distribution of (b0 -•0)/s{b0} 49
               <br/>
               Confidence Interval for •0 49
               <br/>
               2.3 Some Considerations on Making Inferences
               <br/>
               Concerning •0 and •1 50
               <br/>
               Effects of Departures from Normality 50
               <br/>
               Interpretation of Confidence Coefficient
               <br/>
               and Risks of Errors 50
               <br/>
               Spacing of the X Levels 50
               <br/>
               Power of Tests 50
               <br/>
               2.4 Interval Estimation of E{Yh} 52
               <br/>
               Sampling Distribution of ˆY
               <br/>
               h 52
               <br/>
               Sampling Distribution of
               <br/>
               ( ˆY
               <br/>
               h – E{Yh})/s{ ˆY
               <br/>
               h} 54
               <br/>
               Confidence Interval for E{Yh} 54
               <br/>
               2.5 Prediction of New Observation 55
               <br/>
               Prediction Interval for Yh(new) when
               <br/>
               Parameters Known 56
               <br/>
               Prediction Interval for Yh(new) when
               <br/>
               Parameters Unknown 57
               <br/>
               Prediction of Mean of m New Observations
               <br/>
               for Given Xh 60
               <br/>
               2.6 Confidence Band for Regression Line 61
               <br/>
               2.7 Analysis of Variance Approach
               <br/>
               to Regression Analysis 63
               <br/>
               Partitioning of Total Sum of Squares 63
               <br/>
               Breakdown of Degrees of Freedom 66
               <br/>
               x
               <br/>
               Contents xi
               <br/>
               Mean Squares 66
               <br/>
               Analysis of Variance Table 67
               <br/>
               Expected Mean Squares 68
               <br/>
               F Test of •1 = 0 versus •1 _= 0 69
               <br/>
               2.8 General Linear Test Approach 72
               <br/>
               Full Model 72
               <br/>
               Reduced Model 72
               <br/>
               Test Statistic 73
               <br/>
               Summary 73
               <br/>
               2.9 Descriptive Measures of Linear Association
               <br/>
               between X and Y 74
               <br/>
               Coefficient of Determination 74
               <br/>
               Limitations of R2 75
               <br/>
               Coefficient of Correlation 76
               <br/>
               2.10 Considerations in Applying Regression
               <br/>
               Analysis 77
               <br/>
               2.11 Normal Correlation Models 78
               <br/>
               Distinction between Regression and
               <br/>
               Correlation Model 78
               <br/>
               Bivariate Normal Distribution 78
               <br/>
               Conditional Inferences 80
               <br/>
               Inferences on Correlation Coefficients 83
               <br/>
               Spearman Rank Correlation Coefficient 87
               <br/>
               Cited References 89
               <br/>
               Problems 89
               <br/>
               Exercises 97
               <br/>
               Projects 98
               <br/>
               Chapter 3
               <br/>
               Diagnostics and Remedial Measures 100
               <br/>
               3.1 Diagnostics for Predictor Variable 100
               <br/>
               3.2 Residuals 102
               <br/>
               Properties of Residuals 102
               <br/>
               Semistudentized Residuals 103
               <br/>
               Departures from Model to Be Studied by
               <br/>
               Residuals 103
               <br/>
               3.3 Diagnostics for Residuals 103
               <br/>
               Nonlinearity of Regression Function 104
               <br/>
               Nonconstancy of Error Variance 107
               <br/>
               Presence of Outliers 108
               <br/>
               Nonindependence of Error Terms 108
               <br/>
               Nonnormality of Error Terms 110
               <br/>
               Omission of Important Predictor
               <br/>
               Variables 112
               <br/>
               Some Final Comments 114
               <br/>
               3.4 Overview of Tests Involving
               <br/>
               Residuals 114
               <br/>
               Tests for Randomness 114
               <br/>
               Tests for Constancy of Variance 115
               <br/>
               Tests for Outliers 115
               <br/>
               Tests for Normality 115
               <br/>
               3.5 Correlation Test for Normality 115
               <br/>
               3.6 Tests for Constancy of Error
               <br/>
               Variance 116
               <br/>
               Brown-Forsythe Test 116
               <br/>
               Breusch-Pagan Test 118
               <br/>
               3.7 F Test for Lack of Fit 119
               <br/>
               Assumptions 119
               <br/>
               Notation 121
               <br/>
               Full Model 121
               <br/>
               Reduced Model 123
               <br/>
               Test Statistic 123
               <br/>
               ANOVA Table 124
               <br/>
               3.8 Overview of Remedial Measures 127
               <br/>
               Nonlinearity of Regression
               <br/>
               Function 128
               <br/>
               Nonconstancy of Error Variance 128
               <br/>
               Nonindependence of Error Terms 128
               <br/>
               Nonnormality of Error Terms 128
               <br/>
               Omission of Important Predictor
               <br/>
               Variables 129
               <br/>
               Outlying Observations 129
               <br/>
               3.9 Transformations 129
               <br/>
               Transformations for Nonlinear
               <br/>
               Relation Only 129
               <br/>
               Transformations for Nonnormality
               <br/>
               and Unequal Error Variances 132
               <br/>
               Box-Cox Transformations 134
               <br/>
               3.10 Exploration of Shape of Regression
               <br/>
               Function 137
               <br/>
               Lowess Method 138
               <br/>
               Use of Smoothed Curves to Confirm Fitted
               <br/>
               Regression Function 139
               <br/>
               3.11 Case Example—Plutonium
               <br/>
               Measurement 141
               <br/>
               Cited References 146
               <br/>
               Problems 146
               <br/>
               Exercises 151
               <br/>
               Projects 152
               <br/>
               Case Studies 153
               <br/>
               xii Contents
               <br/>
               Chapter 4
               <br/>
               Simultaneous Inferences and Other
               <br/>
               Topics in Regression Analysis 154
               <br/>
               4.1 Joint Estimation of •0 and •1 154
               <br/>
               Need for Joint Estimation 154
               <br/>
               Bonferroni Joint Confidence Intervals 155
               <br/>
               4.2 Simultaneous Estimation of Mean
               <br/>
               Responses 157
               <br/>
               Working-Hotelling Procedure 158
               <br/>
               Bonferroni Procedure 159
               <br/>
               4.3 Simultaneous Prediction Intervals
               <br/>
               for New Observations 160
               <br/>
               4.4 Regression through Origin 161
               <br/>
               Model 161
               <br/>
               Inferences 161
               <br/>
               Important Cautions for Using Regression
               <br/>
               through Origin 164
               <br/>
               4.5 Effects of Measurement Errors 165
               <br/>
               Measurement Errors in Y 165
               <br/>
               Measurement Errors in X 165
               <br/>
               Berkson Model 167
               <br/>
               4.6 Inverse Predictions 168
               <br/>
               4.7 Choice of X Levels 170
               <br/>
               Cited References 172
               <br/>
               Problems 172
               <br/>
               Exercises 175
               <br/>
               Projects 175
               <br/>
               Chapter 5
               <br/>
               Matrix Approach to Simple
               <br/>
               Linear Regression Analysis 176
               <br/>
               5.1 Matrices 176
               <br/>
               Definition of Matrix 176
               <br/>
               Square Matrix 178
               <br/>
               Vector 178
               <br/>
               Transpose 178
               <br/>
               Equality of Matrices 179
               <br/>
               5.2 Matrix Addition and Subtraction 180
               <br/>
               5.3 Matrix Multiplication 182
               <br/>
               Multiplication of a Matrix by a Scalar 182
               <br/>
               Multiplication of a Matrix by a Matrix 182
               <br/>
               5.4 Special Types of Matrices 185
               <br/>
               Symmetric Matrix 185
               <br/>
               Diagonal Matrix 185
               <br/>
               Vector and Matrix with All Elements
               <br/>
               Unity 187
               <br/>
               Zero Vector 187
               <br/>
               5.5 Linear Dependence and Rank
               <br/>
               of Matrix 188
               <br/>
               Linear Dependence 188
               <br/>
               Rank of Matrix 188
               <br/>
               5.6 Inverse of a Matrix 189
               <br/>
               Finding the Inverse 190
               <br/>
               Uses of Inverse Matrix 192
               <br/>
               5.7 Some Basic Results for Matrices 193
               <br/>
               5.8 Random Vectors and Matrices 193
               <br/>
               Expectation of Random Vector or Matrix 193
               <br/>
               Variance-Covariance Matrix
               <br/>
               of Random Vector 194
               <br/>
               Some Basic Results 196
               <br/>
               Multivariate Normal Distribution 196
               <br/>
               5.9 Simple Linear Regression Model
               <br/>
               in Matrix Terms 197
               <br/>
               5.10 Least Squares Estimation
               <br/>
               of Regression Parameters 199
               <br/>
               Normal Equations 199
               <br/>
               Estimated Regression Coefficients 200
               <br/>
               5.11 Fitted Values and Residuals 202
               <br/>
               Fitted Values 202
               <br/>
               Residuals 203
               <br/>
               5.12 Analysis of Variance Results 204
               <br/>
               Sums of Squares 204
               <br/>
               Sums of Squares as Quadratic
               <br/>
               Forms 205
               <br/>
               5.13 Inferences in Regression Analysis 206
               <br/>
               Regression Coefficients 207
               <br/>
               Mean Response 208
               <br/>
               Prediction of New Observation 209
               <br/>
               Cited Reference 209
               <br/>
               Problems 209
               <br/>
               Exercises 212
               <br/>
               PART TWO
               <br/>
               MULTIPLE LINEAR
               <br/>
               REGRESSION 213
               <br/>
               Chapter 6
               <br/>
               Multiple Regression I 214
               <br/>
               6.1 Multiple Regression Models 214
               <br/>
               Contents xiii
               <br/>
               Need for Several Predictor Variables 214
               <br/>
               First-Order Model with Two Predictor
               <br/>
               Variables 215
               <br/>
               First-Order Model with More than Two
               <br/>
               Predictor Variables 217
               <br/>
               General Linear Regression Model 217
               <br/>
               6.2 General Linear Regression Model in Matrix
               <br/>
               Terms 222
               <br/>
               6.3 Estimation of Regression Coefficients 223
               <br/>
               6.4 Fitted Values and Residuals 224
               <br/>
               6.5 Analysis of Variance Results 225
               <br/>
               Sums of Squares and Mean Squares 225
               <br/>
               F Test for Regression Relation 226
               <br/>
               Coefficient of Multiple Determination 226
               <br/>
               Coefficient of Multiple Correlation 227
               <br/>
               6.6 Inferences about Regression
               <br/>
               Parameters 227
               <br/>
               Interval Estimation of •k 228
               <br/>
               Tests for •k 228
               <br/>
               Joint Inferences 228
               <br/>
               6.7 Estimation of Mean Response and
               <br/>
               Prediction of New Observation 229
               <br/>
               Interval Estimation of E{Yh} 229
               <br/>
               Confidence Region for Regression
               <br/>
               Surface 229
               <br/>
               Simultaneous Confidence Intervals for Several
               <br/>
               Mean Responses 230
               <br/>
               Prediction of New Observation Yh(new) 230
               <br/>
               Prediction of Mean of m New Observations
               <br/>
               at Xh 230
               <br/>
               Predictions of g New Observations 231
               <br/>
               Caution about Hidden Extrapolations 231
               <br/>
               6.8 Diagnostics and Remedial Measures 232
               <br/>
               Scatter Plot Matrix 232
               <br/>
               Three-Dimensional Scatter Plots 233
               <br/>
               Residual Plots 233
               <br/>
               Correlation Test for Normality 234
               <br/>
               Brown-Forsythe Test for Constancy of Error
               <br/>
               Variance 234
               <br/>
               Breusch-Pagan Test for Constancy of Error
               <br/>
               Variance 234
               <br/>
               F Test for Lack of Fit 235
               <br/>
               Remedial Measures 236
               <br/>
               6.9 An Example—Multiple Regression with
               <br/>
               Two Predictor Variables 236
               <br/>
               Setting 236
               <br/>
               Basic Calculations 237
               <br/>
               Estimated Regression Function 240
               <br/>
               Fitted Values and Residuals 241
               <br/>
               Analysis of Appropriateness of Model 241
               <br/>
               Analysis of Variance 243
               <br/>
               Estimation of Regression Parameters 245
               <br/>
               Estimation of Mean Response 245
               <br/>
               Prediction Limits for New Observations 247
               <br/>
               Cited Reference 248
               <br/>
               Problems 248
               <br/>
               Exercises 253
               <br/>
               Projects 254
               <br/>
               Chapter 7
               <br/>
               Multiple Regression II 256
               <br/>
               7.1 Extra Sums of Squares 256
               <br/>
               Basic Ideas 256
               <br/>
               Definitions 259
               <br/>
               Decomposition of SSR into Extra Sums
               <br/>
               of Squares 260
               <br/>
               ANOVA Table Containing Decomposition
               <br/>
               of SSR 261
               <br/>
               7.2 Uses of Extra Sums of Squares in Tests for
               <br/>
               Regression Coefficients 263
               <br/>
               Test whether a Single •k = 0 263
               <br/>
               Test whether Several •k = 0 264
               <br/>
               7.3 Summary of Tests Concerning Regression
               <br/>
               Coefficients 266
               <br/>
               Test whether All •k = 0 266
               <br/>
               Test whether a Single •k = 0 267
               <br/>
               Test whether Some •k = 0 267
               <br/>
               Other Tests 268
               <br/>
               7.4 Coefficients of Partial Determination 268
               <br/>
               Two Predictor Variables 269
               <br/>
               General Case 269
               <br/>
               Coefficients of Partial Correlation 270
               <br/>
               7.5 Standardized Multiple Regression
               <br/>
               Model 271
               <br/>
               Roundoff Errors in Normal Equations
               <br/>
               Calculations 271
               <br/>
               Lack of Comparability in Regression
               <br/>
               Coefficients 272
               <br/>
               Correlation Transformation 272
               <br/>
               Standardized Regression Model 273
               <br/>
               X_X Matrix for Transformed Variables 274
               <br/>
               xiv Contents
               <br/>
               Estimated Standardized Regression
               <br/>
               Coefficients 275
               <br/>
               7.6 Multicollinearity and Its Effects 278
               <br/>
               Uncorrelated Predictor Variables 279
               <br/>
               Nature of Problem when Predictor Variables
               <br/>
               Are Perfectly Correlated 281
               <br/>
               Effects of Multicollinearity 283
               <br/>
               Need for More Powerful Diagnostics for
               <br/>
               Multicollinearity 289
               <br/>
               Cited Reference 289
               <br/>
               Problems 289
               <br/>
               Exercise 292
               <br/>
               Projects 293
               <br/>
               Chapter 8
               <br/>
               Regression Models for Quantitative
               <br/>
               and Qualitative Predictors 294
               <br/>
               8.1 Polynomial Regression Models 294
               <br/>
               Uses of Polynomial Models 294
               <br/>
               One Predictor Variable—Second Order 295
               <br/>
               One Predictor Variable—Third Order 296
               <br/>
               One Predictor Variable—Higher Orders 296
               <br/>
               Two Predictor Variables—Second Order 297
               <br/>
               Three Predictor Variables—Second
               <br/>
               Order 298
               <br/>
               Implementation of Polynomial Regression
               <br/>
               Models 298
               <br/>
               Case Example 300
               <br/>
               Some Further Comments on Polynomial
               <br/>
               Regression 305
               <br/>
               8.2 Interaction Regression Models 306
               <br/>
               Interaction Effects 306
               <br/>
               Interpretation of Interaction Regression
               <br/>
               Models with Linear Effects 306
               <br/>
               Interpretation of Interaction Regression
               <br/>
               Models with Curvilinear Effects 309
               <br/>
               Implementation of Interaction Regression
               <br/>
               Models 311
               <br/>
               8.3 Qualitative Predictors 313
               <br/>
               Qualitative Predictor with Two
               <br/>
               Classes 314
               <br/>
               Interpretation of Regression Coefficients 315
               <br/>
               Qualitative Predictor with More than Two
               <br/>
               Classes 318
               <br/>
               Time Series Applications 319
               <br/>
               8.4 Some Considerations in Using Indicator
               <br/>
               Variables 321
               <br/>
               Indicator Variables versus Allocated
               <br/>
               Codes 321
               <br/>
               Indicator Variables versus Quantitative
               <br/>
               Variables 322
               <br/>
               Other Codings for Indicator Variables 323
               <br/>
               8.5 Modeling Interactions between Quantitative
               <br/>
               and Qualitative Predictors 324
               <br/>
               Meaning of Regression Coefficients 324
               <br/>
               8.6 More Complex Models 327
               <br/>
               More than One Qualitative Predictor
               <br/>
               Variable 328
               <br/>
               Qualitative Predictor Variables Only 329
               <br/>
               8.7 Comparison of Two or More Regression
               <br/>
               Functions 329
               <br/>
               Soap Production Lines Example 330
               <br/>
               Instrument Calibration Study Example 334
               <br/>
               Cited Reference 335
               <br/>
               Problems 335
               <br/>
               Exercises 340
               <br/>
               Projects 341
               <br/>
               Case Study 342
               <br/>
               Chapter 9
               <br/>
               Building the Regression Model I:
               <br/>
               Model Selection and Validation 343
               <br/>
               9.1 Overview of Model-Building Process 343
               <br/>
               Data Collection 343
               <br/>
               Data Preparation 346
               <br/>
               Preliminary Model Investigation 346
               <br/>
               Reduction of Explanatory Variables 347
               <br/>
               Model Refinement and Selection 349
               <br/>
               Model Validation 350
               <br/>
               9.2 Surgical Unit Example 350
               <br/>
               9.3 Criteria for Model Selection 353
               <br/>
               R2
               <br/>
               p or SSEp Criterion 354
               <br/>
               R2
               <br/>
               a,p or MSEp Criterion 355
               <br/>
               Mallows’ Cp Criterion 357
               <br/>
               AICp and SBCp Criteria 359
               <br/>
               PRESSp Criterion 360
               <br/>
               9.4 Automatic Search Procedures for Model
               <br/>
               Selection 361
               <br/>
               “Best” Subsets Algorithm 361
               <br/>
               Stepwise Regression Methods 364
               <br/>
               Contents xv
               <br/>
               Forward Stepwise Regression 364
               <br/>
               Other Stepwise Procedures 367
               <br/>
               9.5 Some Final Comments on Automatic
               <br/>
               Model Selection Procedures 368
               <br/>
               9.6 Model Validation 369
               <br/>
               Collection of New Data to Check
               <br/>
               Model 370
               <br/>
               Comparison with Theory, Empirical
               <br/>
               Evidence, or Simulation Results 371
               <br/>
               Data Splitting 372
               <br/>
               Cited References 375
               <br/>
               Problems 376
               <br/>
               Exercise 380
               <br/>
               Projects 381
               <br/>
               Case Studies 382
               <br/>
               Chapter 10
               <br/>
               Building the Regression Model II:
               <br/>
               Diagnostics 384
               <br/>
               10.1 Model Adequacy for a Predictor
               <br/>
               Variable—Added-Variable Plots 384
               <br/>
               10.2 Identifying Outlying Y Observations—
               <br/>
               Studentized Deleted Residuals 390
               <br/>
               Outlying Cases 390
               <br/>
               Residuals and Semistudentized
               <br/>
               Residuals 392
               <br/>
               Hat Matrix 392
               <br/>
               Studentized Residuals 394
               <br/>
               Deleted Residuals 395
               <br/>
               Studentized Deleted Residuals 396
               <br/>
               10.3 Identifying Outlying X Observations—Hat
               <br/>
               Matrix Leverage Values 398
               <br/>
               Use of Hat Matrix for Identifying Outlying
               <br/>
               X Observations 398
               <br/>
               Use of Hat Matrix to Identify Hidden
               <br/>
               Extrapolation 400
               <br/>
               10.4 Identifying Influential Cases—DFFITS,
               <br/>
               Cook’s Distance, and DFBETAS
               <br/>
               Measures 400
               <br/>
               Influence on Single Fitted
               <br/>
               Value—DFFITS 401
               <br/>
               Influence on All Fitted Values—Cook’s
               <br/>
               Distance 402
               <br/>
               Influence on the Regression
               <br/>
               Coefficients—DFBETAS 404
               <br/>
               Influence on Inferences 405
               <br/>
               Some Final Comments 406
               <br/>
               10.5 Multicollinearity Diagnostics—Variance
               <br/>
               Inflation Factor 406
               <br/>
               Informal Diagnostics 407
               <br/>
               Variance Inflation Factor 408
               <br/>
               10.6 Surgical Unit Example—Continued 410
               <br/>
               Cited References 414
               <br/>
               Problems 414
               <br/>
               Exercises 419
               <br/>
               Projects 419
               <br/>
               Case Studies 420
               <br/>
               Chapter 11
               <br/>
               Building the Regression Model III:
               <br/>
               Remedial Measures 421
               <br/>
               11.1 Unequal Error Variances Remedial
               <br/>
               Measures—Weighted Least Squares 421
               <br/>
               Error Variances Known 422
               <br/>
               Error Variances Known up to
               <br/>
               Proportionality Constant 424
               <br/>
               Error Variances Unknown 424
               <br/>
               11.2 Multicollinearity Remedial
               <br/>
               Measures—Ridge Regression 431
               <br/>
               Some Remedial Measures 431
               <br/>
               Ridge Regression 432
               <br/>
               11.3 Remedial Measures for Influential
               <br/>
               Cases—Robust Regression 437
               <br/>
               Robust Regression 438
               <br/>
               IRLS Robust Regression 439
               <br/>
               11.4 Nonparametric Regression: Lowess
               <br/>
               Method and Regression Trees 449
               <br/>
               Lowess Method 449
               <br/>
               Regression Trees 453
               <br/>
               11.5 Remedial Measures for Evaluating
               <br/>
               Precision in Nonstandard
               <br/>
               Situations—Bootstrapping 458
               <br/>
               General Procedure 459
               <br/>
               Bootstrap Sampling 459
               <br/>
               Bootstrap Confidence Intervals 460
               <br/>
               11.6 Case Example—MNDOT Traffic
               <br/>
               Estimation 464
               <br/>
               The AADT Database 464
               <br/>
               Model Development 465
               <br/>
               Weighted Least Squares Estimation 468
               <br/>
               xvi Contents
               <br/>
               Cited References 471
               <br/>
               Problems 472
               <br/>
               Exercises 476
               <br/>
               Projects 476
               <br/>
               Case Studies 480
               <br/>
               Chapter 12
               <br/>
               Autocorrelation in Time
               <br/>
               Series Data 481
               <br/>
               12.1 Problems of Autocorrelation 481
               <br/>
               12.2 First-Order Autoregressive Error
               <br/>
               Model 484
               <br/>
               Simple Linear Regression 484
               <br/>
               Multiple Regression 484
               <br/>
               Properties of Error Terms 485
               <br/>
               12.3 Durbin-Watson Test for
               <br/>
               Autocorrelation 487
               <br/>
               12.4 Remedial Measures for
               <br/>
               Autocorrelation 490
               <br/>
               Addition of Predictor Variables 490
               <br/>
               Use of Transformed Variables 490
               <br/>
               Cochrane-Orcutt Procedure 492
               <br/>
               Hildreth-Lu Procedure 495
               <br/>
               First Differences Procedure 496
               <br/>
               Comparison of Three Methods 498
               <br/>
               12.5 Forecasting with Autocorrelated Error
               <br/>
               Terms 499
               <br/>
               Cited References 502
               <br/>
               Problems 502
               <br/>
               Exercises 507
               <br/>
               Projects 508
               <br/>
               Case Studies 508
               <br/>
               PART THREE
               <br/>
               NONLINEAR REGRESSION 509
               <br/>
               Chapter 13
               <br/>
               Introduction to Nonlinear Regression
               <br/>
               and Neural Networks 510
               <br/>
               13.1 Linear and Nonlinear Regression
               <br/>
               Models 510
               <br/>
               Linear Regression Models 510
               <br/>
               Nonlinear Regression Models 511
               <br/>
               Estimation of Regression Parameters 514
               <br/>
               13.2 Least Squares Estimation in Nonlinear
               <br/>
               Regression 515
               <br/>
               Solution of Normal Equations 517
               <br/>
               Direct Numerical Search—Gauss-Newton
               <br/>
               Method 518
               <br/>
               Other Direct Search Procedures 525
               <br/>
               13.3 Model Building and Diagnostics 526
               <br/>
               13.4 Inferences about Nonlinear Regression
               <br/>
               Parameters 527
               <br/>
               Estimate of Error Term Variance 527
               <br/>
               Large-Sample Theory 528
               <br/>
               When Is Large-Sample Theory
               <br/>
               Applicable? 528
               <br/>
               Interval Estimation of a Single •k 531
               <br/>
               Simultaneous Interval Estimation
               <br/>
               of Several •k 532
               <br/>
               Test Concerning a Single •k 532
               <br/>
               Test Concerning Several •k 533
               <br/>
               13.5 Learning Curve Example 533
               <br/>
               13.6 Introduction to Neural Network
               <br/>
               Modeling 537
               <br/>
               Neural Network Model 537
               <br/>
               Network Representation 540
               <br/>
               Neural Network as Generalization of Linear
               <br/>
               Regression 541
               <br/>
               Parameter Estimation: Penalized Least
               <br/>
               Squares 542
               <br/>
               Example: Ischemic Heart Disease 543
               <br/>
               Model Interpretation and
               <br/>
               Prediction 546
               <br/>
               Some Final Comments on Neural Network
               <br/>
               Modeling 547
               <br/>
               Cited References 547
               <br/>
               Problems 548
               <br/>
               Exercises 552
               <br/>
               Projects 552
               <br/>
               Case Studies 554
               <br/>
               Chapter 14
               <br/>
               Logistic Regression, Poisson Regression,
               <br/>
               and Generalized Linear Models 555
               <br/>
               14.1 Regression Models with Binary Response
               <br/>
               Variable 555
               <br/>
               Meaning of Response Function when
               <br/>
               Outcome Variable Is Binary 556
               <br/>
               Contents xvii
               <br/>
               Special Problems when Response Variable
               <br/>
               Is Binary 557
               <br/>
               14.2 Sigmoidal Response Functions
               <br/>
               for Binary Responses 559
               <br/>
               Probit Mean Response Function 559
               <br/>
               Logistic Mean Response Function 560
               <br/>
               Complementary Log-Log Response
               <br/>
               Function 562
               <br/>
               14.3 Simple Logistic Regression 563
               <br/>
               Simple Logistic Regression Model 563
               <br/>
               Likelihood Function 564
               <br/>
               Maximum Likelihood Estimation 564
               <br/>
               Interpretation of b1 567
               <br/>
               Use of Probit and Complementary Log-Log
               <br/>
               Response Functions 568
               <br/>
               Repeat Observations—Binomial
               <br/>
               Outcomes 568
               <br/>
               14.4 Multiple Logistic Regression 570
               <br/>
               Multiple Logistic Regression Model 570
               <br/>
               Fitting of Model 571
               <br/>
               Polynomial Logistic Regression 575
               <br/>
               14.5 Inferences about Regression
               <br/>
               Parameters 577
               <br/>
               Test Concerning a Single •k: Wald
               <br/>
               Test 578
               <br/>
               Interval Estimation of a Single •k 579
               <br/>
               Test whether Several •k = 0: Likelihood
               <br/>
               Ratio Test 580
               <br/>
               14.6 Automatic Model Selection
               <br/>
               Methods 582
               <br/>
               Model Selection Criteria 582
               <br/>
               Best Subsets Procedures 583
               <br/>
               Stepwise Model Selection 583
               <br/>
               14.7 Tests for Goodness of Fit 586
               <br/>
               Pearson Chi-Square Goodness
               <br/>
               of Fit Test 586
               <br/>
               Deviance Goodness of Fit Test 588
               <br/>
               Hosmer-Lemeshow Goodness
               <br/>
               of Fit Test 589
               <br/>
               14.8 Logistic Regression Diagnostics 591
               <br/>
               Logistic Regression Residuals 591
               <br/>
               Diagnostic Residual Plots 594
               <br/>
               Detection of Influential
               <br/>
               Observations 598
               <br/>
               14.9 Inferences about
               <br/>
               Mean Response 602
               <br/>
               Point Estimator 602
               <br/>
               Interval Estimation 602
               <br/>
               Simultaneous Confidence Intervals for
               <br/>
               Several Mean Responses 603
               <br/>
               14.10 Prediction of a New Observation 604
               <br/>
               Choice of Prediction Rule 604
               <br/>
               Validation of Prediction Error Rate 607
               <br/>
               14.11 Polytomous Logistic Regression for
               <br/>
               Nominal Response 608
               <br/>
               Pregnancy Duration Data
               <br/>
               with Polytomous Response 609
               <br/>
               J – 1 Baseline-Category Logits for
               <br/>
               Nominal Response 610
               <br/>
               Maximum Likelihood Estimation 612
               <br/>
               14.12 Polytomous Logistic Regression
               <br/>
               for Ordinal Response 614
               <br/>
               14.13 Poisson Regression 618
               <br/>
               Poisson Distribution 618
               <br/>
               Poisson Regression Model 619
               <br/>
               Maximum Likelihood Estimation 620
               <br/>
               Model Development 620
               <br/>
               Inferences 621
               <br/>
               14.14 Generalized Linear Models 623
               <br/>
               Cited References 624
               <br/>
               Problems 625
               <br/>
               Exercises 634
               <br/>
               Projects 635
               <br/>
               Case Studies 640
               <br/>
               Appendix A
               <br/>
               Some Basic Results in Probability
               <br/>
               and Statistics
               <br/>
               Appendix B
               <br/>
               Tables
               <br/>
               Appendix C
               <br/>
               Data Sets
               <br/>
               Appendix D
               <br/>
               Selected Bibliography
               <br/>
               Index
               <br/>
               【前言】
               <br/>
               Linear regression models are widely used today in business administration, economics,
               <br/>
               engineering, and the social, health, and biological sciences. Successful applications of
               <br/>
               these models require a sound understanding of both the underlying theory and the practical
               <br/>
               problems that are encountered in using the models in real-life situations. While Applied
               <br/>
               Linear Regression Models, Fourth Edition, is basically an applied book, it seeks to blend
               <br/>
               theory and applications effectively, avoiding the extremes of presenting theory in isolation
               <br/>
               and of giving elements of applications without the needed understanding of the theoretical
               <br/>
               foundations.
              </p>
             </div>
             <!-- .bbp-reply-content -->
            </div>
            <!-- .reply -->
           </li>
           <!-- .bbp-body -->
           <li class="bbp-footer">
            <div class="bbp-reply-author">
             作者
            </div>
            <div class="bbp-reply-content">
             帖子
            </div>
            <!-- .bbp-reply-content -->
           </li>
           <!-- .bbp-footer -->
          </ul>
          <!-- #topic-417233-replies -->
          <div class="bbp-pagination">
           <div class="bbp-pagination-count">
            查看 1 个帖子（总计 1 个）
           </div>
           <div class="bbp-pagination-links">
           </div>
          </div>
          <div class="bbp-no-reply" id="no-reply-417233">
           <div class="bbp-template-notice">
            <p>
             您必须先登录才能回复该主题。
            </p>
           </div>
          </div>
         </div>
        </div>
        <!-- .entry-content -->
        <footer class="entry-meta">
        </footer>
        <!-- .entry-meta -->
       </article>
       <!-- #post -->
       <div class="comments-area" id="comments">
       </div>
       <!-- #comments -->
      </div>
      <!-- #content -->
     </div>
     <!-- #primary -->
     <div class="sidebar-container" id="tertiary" role="complementary">
      <div class="sidebar-inner">
       <div class="widget-area">
        <aside class="widget bbp_widget_login" id="bbp_login_widget-2">
         <h3 class="widget-title">
          登录
         </h3>
         <form action="http://cos.name/cn/wp-login.php" class="bbp-login-form" method="post">
          <fieldset>
           <legend>
            登录
           </legend>
           <div class="bbp-username">
            <label for="user_login">
             用户名:
            </label>
           </div>
           <div class="bbp-password">
            <label for="user_pass">
             密码:
            </label>
           </div>
           <div class="bbp-remember-me">
            <label for="rememberme">
             记住用户名
            </label>
           </div>
           <div class="bbp-submit-wrapper">
            <button class="button submit user-submit" id="user-submit" name="user-submit" tabindex="104" type="submit">
             登录
            </button>
           </div>
           <div class="bbp-login-links">
            <a class="bbp-register-link" href="http://cos.name/cn/wp-login.php?action=register" title="注册">
             注册
            </a>
            <a class="bbp-lostpass-link" href="http://cos.name/cn/wp-login.php?action=lostpassword" title="忘记密码">
             忘记密码
            </a>
           </div>
          </fieldset>
         </form>
        </aside>
        <aside class="widget widget_text" id="text-7">
         <h3 class="widget-title">
          搜索
         </h3>
         <div class="textwidget">
          <form action="http://www.google.com/search" id="bbp-search-form" method="get" onsubmit="Gsitesearch(this)" role="search">
           <div>
           </div>
          </form>
          <form id="bbp-search-form-baidu" onsubmit="g(this)" role="search">
           <div>
           </div>
          </form>
         </div>
        </aside>
        <aside class="widget widget_text" id="text-2">
         <h3 class="widget-title">
          新鲜事
         </h3>
         <div class="textwidget">
          <ul>
           <li>
            <a href="http://cos.name/cn/topics/">
             最新帖子
            </a>
           </li>
           <li>
            <a href="http://cos.name/cn/view/popular/">
             最热门主题
            </a>
           </li>
           <li>
            <a href="http://cos.name/cn/view/no-replies/">
             消灭零回复
            </a>
           </li>
          </ul>
         </div>
        </aside>
        <aside class="widget widget_text" id="text-3">
         <h3 class="widget-title">
          RSS订阅
         </h3>
         <div class="textwidget">
          <ul>
           <li>
            <img src="http://cos.name/wp-includes/images/rss.png"/>
            <a href="http://cos.name/cn/topics/feed/">
             所有主题
            </a>
           </li>
           <li>
            <img src="http://cos.name/wp-includes/images/rss.png"/>
            <a href="http://cos.name/cn/forums/feed/">
             所有帖子
            </a>
           </li>
          </ul>
         </div>
        </aside>
       </div>
       <!-- .widget-area -->
      </div>
      <!-- .sidebar-inner -->
     </div>
     <!-- #tertiary -->
    </div>
    <!-- #main -->
    <footer class="site-footer" id="colophon" role="contentinfo">
     <div class="site-info">
      版权所有 © 2014 统计之都 | 由
      <a href="http://wordpress.org/">
       WordPress
      </a>
      构建 | 主题修改自
      <a href="http://wordpress.org/themes/twentythirteen">
       Twenty Thirteen
      </a>
     </div>
     <!-- .site-info -->
    </footer>
    <!-- #colophon -->
   </div>
   <!-- #page -->
  </div>
 </body>
</html>